Working on a job that was really kicking my ass, I just blurted out - "this is too hard, and you shouldn't be working on it."

Lacey, the community manager, didn't jump in to save me.

I let the dead air sit for a second. It was okay - that's the purpose of the worklab. You have to run into dead-ends. You have to work to failure so that the students see that it happens to all of us.

You have to fail. And then you have to keep going.

But this one was just too hard. I think I could have cracked it given time, but it would take phone calls and referrals and more time.

There was no easy fix to find a SAP VMS IS-Auto candidate, and I wanted to make sure that the students knew that this is normal.

So this is what I said:

"Maybe it's me. This job is taking too long, and I don't want to work on it. And that's okay. Now, you don't always have that option, but this is a job posted by 14 other agencies. If you did find someone to do this, you could place that person anywhere in a heartbeat.

And that's a lesson you have to learn. There are some jobs you shouldn't work on. Your real challenge is getting good enough to know when to stop.

Look - if you find this person, you get a placement. But how long would it take you?

We're moving into a candidate market - and the number one problem we face is not selling enough in this market. Having a job is not enough. You need a good job, that you can fill, and options on how to sell them.

That's not easy to talk about. It's nearly impossible if you work inside, but you burn out recruiters when you have them work on impossible to fill reqs.

And - well - the problem is that you make your money when you find the harder to fill roles. Crap. I really need a graph for this, and I don't want you going back to your bosses and telling them you don't want to work on their jobs.

But you need to learn what is possible. But that's a rant for another day. Let's move on..."

Jim Durbin returns to St Louis with all new recruiting techniques and a clear view of how technology like artificial intelligence changes the industry in the next year.

On September 25th in Kansas City, and on September 26th in St Louis, Jim will deliver a dual session training. Session 1 is new techniques for LinkedIn, Facebook, and resume search. Session 2 is a reveal of the AI tools that will hit or are already on the market for recruiting.

Powerful Recruiting with LinkedIn and Facebook:

The Social Media Headhunter presents a 90 minute super-session on the new LinkedIn UI, Facebook search, and his 360 sourcing method for job boards, databases, and social sites. .

In this presentation, you'll learn:

* The fastest way to search using the new keyword search* Search strings to build company and title lists* Short Boolean - the key to 360 Sourcing

* Facebook search

* The best messages for contacting candidates by email, text, and InMail. * Meetup, Behance, Github, and more* How to use Indeed and Glassdoor to find candidates without resumes

WHAT THEY'RE SAYING

"Just to let you know, this was one of the best webinars I've ever attended."- Michael B.

"Jim Durbin is an awesome presenter! Definitely learned a lot! I was writing names down as fast as I could! LOL!"- Susie L.

"In all my 12+ years of recruiting, I have never experienced a training so thorough on how to make candidates come to me!"- Abraham B.

BIO:

Jim Durbin is the Founder of SourcingWorklab.com. As a marketer and recruiter, he's been placing people since 1999. In the last decade, he's been known as the Social Media Headhunter, where he's trained over 9,000 recruiters on digital tools. He runs a B2B marketing firm, develops startups in the AI and recruiting space, and takes as many full desk searches as he can get his hands on. He predicted the demise of LinkedIn in 3 years, quit Twitter in 2016, and writes regularly in industry publications and on his blog, DigitalMarketingHeadhunter.com

I'm "this" close to launching a search for a social strategist. Just working out the details. It's the kind of social job you dream of - strategy, but working under a great boss with real marketing experience.

What kind of social? Glad you asked. It's important that you ask, because you're supposed to know there are different kinds by now.

It's a mix of customer service and branding. The client has customer service needs, and wants to see what social can and should be doing for them, but they also want to extend and expand their branding and lead generation. You're not selling online - but you are generating leads and working to explain how to best respond to your customer base.

Here's some boilerplate I made up.

Your role is a new one, designed to increase the capabilities, performance, and tracking of social data that includes Facebook, Twitter, LinkedIn, Instagram, YouTube and other popular channels. As a strategist, you’ll work with the marketing and customer service teams to engage with our members, enhance branding campaigns, and integrate with product and service teams. This position does not have direct reports.

Experience with regulated industries is a plus. They're innovative, but not the wild west - and you don't want to have to explain to the execs why someone on Facebook is yelling at you.

Description:

-Design a comprehensive social media strategy

-Create and manage best practices for customer service and corporate communications.

-Listen and engage in relevant social discussions involving the brand and the industry.

-Plan and execute regular social promotions and campaigns along with performance metrics and results.

-Develop social media content strategies and editorial calendars.

You know the rest of the drill. Find me if you're interested. I mainly post these to prove I still exist, and so I have something to send you if you ask me what the job entails.

You have to be in Dallas. You're not going to make six figures. This isn't a job posting to Facebook. And agency experience or corporate experience is important - it's not a job for freelancers or consultants.

In the last 9 years, with the help of Kathy Simmons of Experts Connection, I've delivered paid webinar training to over 9,000 recruiters on the topics of LinkedIn, Facebook, Twitter, and the digital searching.

These top rated webinars come with a 90 minute session followed by live Q&A, a video download, the slide deck as a PDF, and if you register before April 20th, I'll send you me new LinkedIn UI search guide.

In the dawn of the new century, we laughed at the idea that machines could think.

We played with them. We paid for them to get smarter.

Years of the relentless drumbeat of disruptive technology numbed us to the real danger. The machines weren’t taking over. We, were becoming the machines.

Blinded by the sheer power of gigantic lists, we begin to think and act like pale algorithmic copies of the software that we thought were making us into something better. Something we thought was making us, more than human.

As we grew more faster, more capable, and more stalky, our prospects rebelled. They put up what we can only call, a resistance, to our well crafted templates and phone messages and social entreaties. In our amazement at the power of our new human-machine intelligence gathering, we forgot the greatest app was simply, ourselves.

And so as we embrace the new machine-hybrid world, the resistance has struck deep into the core of our existence. They only speak when they wish to be spoken to! Having escaped our plans for data supremacy - we must finally and completely embrace our human side - we must become, once again - the flesh and blood heroes of old.

I fear no machine! I fear when men cast off their humanity and function like machines.

Or rather, I fear for their jobs, because people who act like machines are easily replaced by machines.

HYPOTHESIS: In the next five years, Facebook will turn away from being a social network and consider itself solely to be the operating system of the internet.

Algorithms will drive the death of social networks, because what an algorithm can track is not of interest to human beings. Instead of being useful, algorithms track what we do - contributing to wasted time and personal dysfunction.

The value of social traffic is already declining. Advertising is addicted to traffic, but the value of paying traffic is high. The value of traffic that does not convert is close to zero for all but the largest of brands. Advertising is struggling to prove its worth, but as the move towards pixel conversion grows stronger, the emptiness of "social," "video" and "images" in short bursts will cause Facebook to pivot to run in the background instead of being a news source.

In simple explanation - trending news that points to twitter hashtags, shocking headlines, or stolen reddit posts has a net negative value on a brand. Declining click-through rates will eventually lead Facebook to abandon generic advertising through a news feed.

The future of social is limited social groups with strong privacy controls. Facebook, who has already squashed the other social networks (Twitter and Snapchat are dead, even if they don't know it), will recognize this and seek to be the internet login, running apps in the background that function like a personal enterprise software suite.

The hot buzz around AI is the replacement of human workers with robots or AI. I'm not worried, for several reasons. The generics are that AI isn't really AI, and by the time it is, it will replace almost all jobs. The second is that the people programming machine learning don't seem to understand recruiting.

For the purposes of this defense, I'll use AI/machine learning/and computer screening as the same idea.

The data points that are gathered are very useful for a system, but I'm highly suspicious of the claims that they can screen better than a human. These claims are based on, quite frankly, terrible screening processes. For a chat bot to work, it has to be fed the right information. Maybe I'm missing the good ones, but I'm not seeing any example of excellent screening. I'm seeing poor screening practices replicated in these bots. An analogy would be a drop-down box in a job board asking that applicants have 2 years of experience. This sounds like a good idea, but in practice, has a value very close to zero. For AI to replace recruiters, it will have to actually replace recruiters, not just automate portions of the hiring process. Gathering data is only useful if it affects the outcome, and is not counted as an advantage of AI.

This is intended as an apologia, a formal defense of my opinions. I believe that in the process, the action, and the skillset, recruiters cannot be replaced. The most likely scenario is augmentation for the collection of data and the reduction of paperwork.

Recruiting is not the same as hiring. It's value lies in social proof and time saved.

The primary definition of recruiting is making an introduction between two individuals who are poorly trained to interview. The social proof of a recruiter introduction is useful in calming fear and creating certainty. In a mature recruiting model, the hiring committee receives screened candidates who have already been vetted. This means the hiring committee should not be asking basic questions of motivation, experience, and suitability.

*All people are poorly trained to interview or be interviewed. There is no formal process, no testing, and not enough actions to constitute good interviewers or good interviewees. Belief in superior interviewing skills for a task that constitutes a very small portion of time worked is delusional. An outcome of getting hired or hiring is simply not a good indicator of interview quality. It should also be noted that a good interviewer (a recruiter), does not translate into being good at being interviewed.

A computer screen would have to demonstrate superior screening to the hiring committee, but also social proof. Passing a test is not the same as passing a human screen. Unless companies are willing to train managers to ignore their social conditioning, the recruiter introduction will be of more value than an AI screen. I should also note that many companies utilize referral systems and rank referrals as a high quality of hire. If AI were to replace recruiters, this would eliminate the belief in and the practice of referral-based hiring. After all, if social proof is less valuable than an AI screen, then a referral is less valuable than an AI screen.

Defense of AI is an attack on referrals, because recruiting is a form of referral-based hiring.

Matching speech patterns

Another way humans generate comfort lies in connecting the rhythm of our speech patterns to our brain waves. Two humans speaking to each other literally reach a sweet spot in their conversation where their brain is functioning on the same bandwidth. A person cannot understand you unless their brain can match your speech, including tone, speed, words, and volume to their experience. Again, humans are highly adaptable in listening to each other. Computers are uni-directional in this manner. The cues that tell us that we are being understood are not present in an AI interface, and cannot be. To be successful, an AI at a minimum has to be able to adopt and adapt speech patterns to generate a good conversation.

In the absence of this skill, the candidate will be assigning a large portion of their conscious processing to thinking past the screen. Instead of exploring and discussing their value, they are seeking to tell a story that will survive the computer and create good marks in the eyes of an unseen human reviewer.

Consistency is vital in the decision-making process

A human being making a decision has several well-known triggers. Verbal and written statements to a human being rank highly in terms of creating consistency. Typing answers on a computer is not a trigger. This principle is known as disinhibition. While engaging with an AI bot, the human is not making statements they feel compelled to live up to. Without the non-verbal cues of a conversation (including phone calls), a job-seeker is not making positive statements about the company, the interview or a job. Those positive statements are a major cause of decision making later in the process. Any human-computer interface would have to mimic a large portion of non-verbal clues including facial tics, presence, breathing, rhythm, and mirroring to generate a strong response from the candidate.

It is possible to program these cues, but the likelihood of mistakes due to what is known as the "uncanny valley" is not being pursued. If a robot interface is too human, we recoil. If it is not human enough, we don't care what it thinks. Failing to understand behavioral science is a major flaw in AI systems. We don't recognize how good humans are at screening each other, something that no AI can replicate.

Current AI's are sterile, voice recognition is bad, and translation of slang is nearly impossible.

An automated interface is by definition, sterile. Research into human-like robots and avatars shows improvement in certain kinds of information, but that information has to be carefully curated and applied to a working "AI-interface" that has sufficient voice recognition software and around 40% of the facial reflexes of a human. No chat bot is doing that now because quite frankly, it's a different skill set than writing an AI logic engine.

The attempt to match language translation and dialects only works in a laboratory. It's similar to showing off your fancy software on your desktop configured to run your software. That you can make it work is not the same as testing it in the field. If human beings struggle to understand each other, with accents, lingo, mistaken phrases, and even the way in which we think, how could a computer be any different? When we look at voice activated software, we forget that we have to train the software to understand us, or we have to fit into a comfortable middle ground of dialect.

The medium matters as well. Chat and text and voice and email and the eventual computer interface don't match up to expectations. The amount of logic necessary for the AI to learn requires a true AI, which again, is not what is offered today or in the near future.

We don't know why we hire

Research into quality of hire is simply not conclusive. In order to replace a functioning part of the hiring process, we would have to better understand hiring. We are literally at the leeches to draw blood stage in our understanding of why people make decisions. Confirmation bias and the role of empathy could be big factors in the success of an employee. In short - if enough people are involved and want the employee to succeed, their chances of succeeding are probably improved. Removing human contact at any stage could very well lead to disastrous hiring.

And worse, we could be masking the effects with "good data." Team chemistry is something we can observe, the same way we can observe communication networks or social messaging. There is no one who has, as of yet, figured out to create a network that improves on communication, and there is no one who has figured out to high create high-performing teams.

Pretending that large amounts of data and reason can lead us to successful hires is a nice fiction we peddle to sell books and explain success.

Screening is by definition one-sided, and susceptible to changes in the market

Finally - screening is a very one-sided view of the hiring process. Companies screen jobseekers, suggesting a power differential that is one-sided. In that design, companies get to choose what they like and don't like. As supply and demand of qualified workers rises and fall in tune with technology, economic health, and generational changes, the power differential swings back to the jobseeker, and screening is seen as demeaning and useful only to the company. This is always true in high demand positions, where executives, top programmers, and top salespeople don't feel the need to participate in screening processes.

AI screening has to be useful to the jobseeker, whether they get the job or not, or it will be seen as a net negative, accidentally leaving out top performers and instead only delivering minimally qualified candidates who are willing to be shepherded into a digital cattle call.

Summary:AI is fantastic and replacing poor processes. Those involved in paperwork, scheduling, basic screening and process notifications can and will be replaced, but the gains in productivity are actually the removal of loss of productivity from too much data. AI solves the problem of digital application. It does little to solve the problem of recruiting. Recruiting is a human function that is often mistaken for data entry and collection. The industry will shrink, as process recruiters (mostly internal) are replaced by software. This is not a threat to recruiters whose primary purpose is contact with jobseekers.